Welcome to the June edition of the ‚Machine-Learning-Collage' series, where I provide an overview of the different Deep Learning research streams. So what is a ML collage? Simply put, I draft one-slide visual summaries of one of my favourite recent papers. At the end of the month all of the resulting visual collages are collected in a summary blog post. Thereby, I hope to give you a visual and intuitive deep dive into some of the coolest trends. May has been quite the month including the virtual ICLR 2021 conference, ICML review decisions as well as the NeurIPS deadlines.

Using the MaskedFace-Net and Flickr Faces datasets, we resized the images to be 128x128 to save space. We moved images into three folders (train, val, and test). Each folder had three subdirectories (correct/incorrect/no_mask). This allowed them to be easily loaded with PyTorch's torchvision. If you look at the code snippet, you'll see that we used torchvision's built-in arguments to apply transformations to our images.

GPT3 is an advanced language model. Have hands on experience with GPT3 and learn what is GPT-3 in this article in depth here

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images.

Artificial Intelligence refers to machine intelligence that operates the same way human intelligence works illustrating conscience and emotions. Nowadays computers are trained enough to play with humans and not only they can play but can compete with humans and are capable of winning. This all is so possible because the machine is powered by Artificial Intelligence. In today's scenario, the machines are continuously been modified to adapt to the capabilities of the human. The machine is embedded with AI to follow the humans in terms of their vision, their perception, and even recognition of the Image known as visual perception, speech recognition, Natural Language Processing (NLP) and so many tasks.

Emojis are a wonderful method to express oneself. This deep learning project automatically predicts emojis based on a given phrase. Stay updated with latest technology trends Join DataFlair on Telegram!! In this machine learning project, we predict the emoji from the given text. This means we build a text classifier that returns an emoji that suits the given text.

Last week, Google Research held an online workshop on the conceptual understanding of deep learning. The workshop, which featured presentations by award-winning computer scientists and neuroscientists, discussed how new findings in deep learning and neuroscience can help create better artificial intelligence systems. While all the presentations and discussions were worth watching (and I might revisit them again in the coming weeks), one in particular stood out for me: A talk on word representations in the brain by Christos Papadimitriou, professor of computer science at Columbia University. In his presentation, Papadimitriou, a recipient of the Gödel Prize and Knuth Prize, discussed how our growing understanding of information-processing mechanisms in the brain might help create algorithms that are more robust in understanding and engaging in conversations. Papadimitriou presented a simple and efficient model that explains how different areas of the brain inter-communicate to solve cognitive problems.

Since the May 2020 release of OpenAI's GPT-3, AI researchers have embraced super-large-scale pretraining models. Packing an epoch-making 175 billion parameters, GPT-3 has achieved excellent performance across multiple natural language processing (NLP) tasks. Despite their size and power however, such models still lack common sense or cognitive abilities, and so struggle with complex reasoning tasks like open dialogue, knowledge-based Q&A, visual reasoning, etc. In a bid to promote the research and development of China's own large-scale pretraining models and further explore universal intelligence from a more fundamental perspective, the Beijing Academy of Artificial Intelligence (BAAI) recently unveiled Wu Dao 1.0, China's first homegrown super-scale intelligent model system. The work was led by BAAI Research Academic Vice President and Tsinghua University Professor Tang Jie, with contributions from a team of more than 100 AI scientists from Peking University, Tsinghua University, Renmin University of China, Chinese Academy of Sciences and other institutes.

Editor's Note: Multi-objective optimization (MOO) is used for many products at LinkedIn (such as the homepage feed) to help balance different behaviors in our ecosystem. There are two parts to how we work with multiple objectives: the first is about training high-fidelity models to predict member behavior (e.g., probability a member will click an article). The second is around trading off different objectives for a unified member experience based on utility to the LinkedIn ecosystem (e.g., a comment is much more valuable than a click). This post will focus on the first part of multi-objective optimization, where we utilize a multi-task, deep learning model to create higher fidelity consumption models; for more information on the second part, objective tradeoffs, see this article from KDnuggets about automatically tuning this tradeoff for faster model iteration. LinkedIn's members rely on the homepage feed for a variety of content including updates from their network, industry articles, and new job opportunities.

HUAWEI CLOUD's Enterprise Intelligence (EI) has achieved strong results in numerous industry competitions and evaluations. HUAWEI CLOUD has invested heavily in basic research AI in three domains: computer vision, speech and semantics, and decision optimization. To help AI empower all industries, the ModelArts enabling platform supports plug-and-play deployment of HUAWEI CLOUD's research results in areas such as automatic machine learning, small sample learning, federated learning, and pre-training models. In the area of perception, HUAWEI CLOUD continues to be an industry-leader in ImageNet large-scale image classification, WebVision large-scale network image classification, MS-COCO two-dimensional object detection, nuScenes three-dimensional object detection, and visual pre-training model verification, including downstream classification, detection, and segmentation. Perception models driven by ModelArts have been widely used in sectors such as medical image analysis, oil and gas exploration, and fault detection in manufacturing. In cognition, HUAWEI CLOUD integrates industry data based on its expertise in semantic analysis and knowledge graphs.